How to create “humble” AI
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.
Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.
Researchers at MIT, Mass General Brigham, and Harvard Medical School developed a deep-learning model to forecast a patient’s heart failure prognosis up to a year in advance.
MIT computer science students design AI chatbots to help young users become more social, and socially confident.
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
A new approach could help users know whether to trust a model’s predictions in safety-critical applications like health care and autonomous driving.
The approach could help engineers tackle extremely complex design problems, from power grid optimization to vehicle design.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.